X. Wang

In recent years, the distributed architecture has been widely adopted by security companies with the rapid expansion of their business. A distributed system is comprised of many computing nodes of different components which are connected by high-speed communication networks. With the increasing functionality and complexity of the systems, failures of nodes are inevitable which may result in considerable loss. In order to identify anomalies of the possible failures and enable DevOps engineers to operate in advance, this paper proposes a two-layer prediction architecture based on the monitoring sequences of nodes status. Generally speaking, in the first layer, we make use of EXPoSE anomaly detection technique to derive anomaly scores in constant time which are then used as input data for ensemble learning in the second layer. Experiments are conducted on the data provided by one of the largest security companies, and the results demonstrate the predictability of the proposed approach.

Classic of Changes is a Chinese cultural classic born more than 3000 years ago. Its profound philosophical thoughts and the use of divination have brought Classic of Changes to a strong oriental mysticism. The view of the heaven and man of yin and yang and the five elements states of Classic of Changes are completely different from the Western elemental theory of ancient Greece. The latter gave birth to classical and modern scientific theories, and the yin and yang and the eight trigrams symbol has become synonymous with oriental mysticism. In fact, the cosmology of the Holism of Classic of Changes is a precious scientific heritage of mankind. The axiomatization of the symbolic formal system of Classic of Changes aims to unveil the veil of oriental mysticism and provide oriental wisdom for the development of modern science. Transforming the symbolic system of Classic of Changes into a formal axiom system is the crystallization of the fusion of wisdom between the East and the West. The axiomatization of the symbolic formal system of Classic of Changes shows that the oriental and the western scientific tradition harmony but not sameness and there is no conflict. Classic of Changes can also be interpreted by the axiomatic system like Euclid’s Elements. The main contribution of this paper is that the author skillfully uses mathematical language to formulate the system of Classic of Changes, reconstructs the ideological system of Classic of Changes with the axiomatic method and realizes the scientificalization of Chinese classical natural philosophy. The formal axiom system of Classic of Changes may give us such a revelation: the gap between the oriental and western scientific traditions is mainly in the axiomatization, but the lack of oriental scientific tradition may be the bottleneck of the development of modern science.

In this paper, I discussed the possibility to argue for a human right to internet access in Confucian society. I argued firstly that Confucianism could properly accommodate the concept of human rights, even though it does not have an explicit term for it. Secondly, Confucian concept of min xin, as a similar concept of democracy with differences, is used in Confucianism as a normative concept to lay the foundation of the state and legitimatize the governance. Last but not least, I argued that the roles that min xin are supposed to play in reality are never fully carried out due to the specific hierarchical information structure of Confucian society. I proposed that with internet, the concept of min xin would have to be able to play its roles properly. As such, the arguments that disregard internet access by referting to the protection of the Confucian values might not stand.

An integrated hog futures price forecasting model based on whale optimization algorithm, LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is used to optimize the LightGBM model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices.

Nonlinear time series denoising is the prerequisite for extracting effective information from observation sequence. An effective chaotic signal denoising method not only has a good signal-to-noise ratio enhancement performance, but also can remain as a good unpredictable denoised signal. However, the inherent characteristics of chaos, such as extreme sensitivity to initial values and broadband spectrum, pose challenges for noise reduction of polluted chaotic signals. To address these issues, an adaptive smoothing multiscale morphological filtering is proposed to reconstruct chaotic signals. In the process of noise reduction for contaminated chaotic signals, firstly, a multiscale morphological filter is constructed adaptively according to the multiscale permutation entropy of morphological filter residuals, and the contaminated signals are filtered. Secondly, the weight coefficients of scale structural element are calculated by the residual sum of squares operation, and the chaotic signals are reconstructed. Finally, the resampled filter signals are smoothed by cubic B-spline interpolation operation. In the experiment, the Lorenz signal with white Gaussian noise, the measured sunspot, and the chaotic vibration signal are reconstructed by four comparison methods. The test results show that the proposed ASMMF method has obvious advantages in noise suppression and topological trajectory restoration.

Historical progress is a core belief of the Enlightenment and modernity, also a spiritual catalyst of human emancipation in the past centuries. However, due to the naive understanding of scholars and its misuse by political power, the idea of progress has fallen from a realistic political belief in the pursuit of liberty and democracy to a metaphysical faith and a one-sided ideology. Instead of abandoning the concept itself, this paper will provide a new version for progress. In this version, supported theoretically with ideas from a Marxian critique of the paradox of progress in capitalist society and a Habermasian reconstruction of social evolution and progress, progress shall not be understood as an intrinsic trend of history itself, but a “historical-practical project” of humanity. The intent of rewriting progress is to transcend the dilemma between progressivism and catastrophism while at the same time preserving its positive meaning.

ObjectiveThere is little literature on the validity of kurtosis-adjusted noise energy metrics in human studies. Therefore, this study aimed to validate the application of cumulative noise exposure adjusted by kurtosis in evaluating occupational hearing loss associated with non-Gaussian noise among manufacturing workers.MethodsA cross-sectional survey was conducted on 1,558 manufacturing workers exposed to noise from five industries to collect noise exposure and hearing loss data. Both CNE and kurtosis-adjusted CNE were collapsed into 2-dB∙year bins, and the mean noise-induced permanent threshold shifts at 3, 4, and 6 kHz in each bin were calculated. The contributions of CNE and CNE′ to noise-induced hearing loss were compared using the multiple linear regression. The degree of overlap of two linear regression equations was used to evaluate the validity of the CNE′ using a stratified analysis based on age and sex.ResultsMultiple linear regression models showed that after kurtosis adjustment, the standardized regression coefficient of CNE increased from 0.230 to 0.255, and R2 increased from 0.147 to 0.153. The linear relationship between NIPTS346 and CNE′ or CNE showed that the regression line of non-Gaussian noise was closer to that of Gaussian noise when using CNE′ than using CNE. The mean difference in NIPTS346 between the equations of non-Gaussian noise and Gaussian noise was significantly reduced from 4.32 to 1.63 dB HL after kurtosis adjustment. Through a stratified analysis, these significant decreases were observed in male and female workers, and workers aged ≥30 years old.ConclusionAs a noise exposure metric combining noise energy and temporal characteristics, the kurtosis-adjusted-CNE metric was more effective than CNE alone in assessing occupational hearing loss among manufacturing workers in non-Gaussian noise environment. However, more studies are needed to verify the validity of the kurtosis-adjusted-CNE metric.

The direction and specificity of endolysosomal membrane trafficking is tightly regulated by various cytosolic and membrane‐bound factors, including soluble NSF attachment protein receptors (SNAREs), Rab GTPases, and phosphoinositides. Another trafficking regulatory factor is juxta‐organellar Ca2+, which is hypothesized to be released from the lumen of endolysosomes and to be present at higher concentrations near fusion/fission sites. The recent identification and characterization of several Ca2+ channel proteins from endolysosomal membranes has provided a unique opportunity to examine the roles of Ca2+ and Ca2+ channels in the membrane trafficking of endolysosomes. SNAREs, Rab GTPases, and phosphoinositides have been reported to regulate plasma membrane ion channels, thereby suggesting that these trafficking regulators may also modulate endolysosomal dynamics by controlling Ca2+ flux across endolysosomal membranes. In this paper, we discuss the roles of phosphoinositides, Ca2+, and potential interactions between endolysosomal Ca2+ channels and phosphoinositides in endolysosomal dynamics.

ObjectiveBasic combat training is a kind of necessary high-intensity training to help each military recruit convert into a qualified soldier. In China, both the physical fatigue and passive psychological state have been observed in new recruits during BCT. However, after same-intensity training, the degree of fatigue and passive mood vary among recruits. Therefore, this study aimed to explore the effect of BCT on mood state of recruits with different physical fitness levels from a perspective of fatigue.Materials and methodsBefore and after BCT, the degree of fatigue and mood state of participants were evaluated via the Borg Rating of Perceived Exertion Scale and Profile of Mood States Questionnaire immediately after 20 push-ups as RPE and POMS scores [total mood disturbance, passive mood and positive mood ]. The participants were divided into two groups according to the RPE score measured after BCT: group 1: RPE score after BCT < 13 and group 2: RPE score after BCT ≥ 13.ResultA total of 564 recruits were included. After BCT, in group 1, TMD and passive mood Ttension, Tfatigue, Tdepression, Tconfusion declined significantly, but positive mood both increased significantly ; while in group 2, only Tanger and Testeem increased significantly.ConclusionBCT could alleviate passive mood and add to positive mood for recruits with better physical fitness, while had no ameliorative effects on or even deteriorate most of the passive mood for recruits with worse physical fitness.

Linear regression and its variants have been widely used for classification problems. However, they usually predefine a strict binary label matrix which has no freedom to fit the samples. In addition, they cannot deal with complex real-world applications such as the case of face recognition where samples may not be linearly separable owing to varying poses, expressions, and illumination conditions. Therefore, in this paper, we propose the kernel negative ε dragging linear regression method for robust classification on noised and nonlinear data. First, a technique called negative ε dragging is introduced for relaxing class labels and is integrated into the LR model for classification to properly treat the class margin of conventional linear regressions for obtaining robust result. Then, the data is implicitly mapped into a high dimensional kernel space by using the nonlinear mapping determined by a kernel function to make the data more linearly separable. Finally, our obtained KNDLR method is able to partially alleviate the problem of overfitting and can perform classification well for noised and deformable data. Experimental results show that the KNDLR classification algorithm obtains greater generalization performance and leads to better robust classification decision.

Responding to serious environmental problems, requires urgent and fundamental shifts in our day-to-day lifestyles. This paper employs a qualitative, cross-cultural approach to explore people’s subjective self-reflections on their experiences of pro-environmental behavioral spillover in three countries; Brazil, China, and Denmark. Behavioral spillover is an appealing yet elusive phenomenon, but offers a potential way of encouraging wider, voluntary lifestyle shifts beyond the scope of single behavior change interventions. Behavioral spillover theory proposes that engaging in one pro-environmental action can catalyze the performance of others. To date, evidence for the phenomenon has been mixed, and the causal processes governing relationships between behaviors appear complex, inconsistent and only partly understood. This paper addresses a gap in the literature by investigating accounts of behavioral spillover in three diverse cultural settings using qualitative semi-structured interviews. The analysis shows that while around half of participants overall who were questioned recalled spillover effects, the other half had not consciously experienced spillover. There were few significant differences across cultures, though some forms of spillover effects were reported more in some cultures than others. More environmentally-engaged participants across all three countries were significantly more likely to experience spillover than those who were less engaged. Accounts of within-domain spillovers were most commonly reported, mainly comprising waste, resource conservation and consumption-related actions. Accounts of between-domain spillover were very rare. Recollection of contextual and interpersonal spillover effects also emerged from the interviews. Our findings suggest that more conscious behavioral spillover pathways may be limited to those with pre-existing environmental values. Behavioral spillover may comprise multiple pathways incorporating conscious and unconscious processes. We conclude that targeting behavioral catalysts that generate more socially diffuse spillover effects could offer more potential than conventional spillover involving a single individual.

Using U.S. listed firms during the period from 1971 to 2010, this paper investigates the effect of religion on corporate cost behavior. We find that religion mitigates cost stickiness induced by agency or behavioral biases of managers. This result holds for several robustness tests that address endogeneity concerns. The mitigating effect of religion on cost stickiness is through the channel of reducing top managers’ overconfidence and optimistic bias regarding future demand change and promoting managers’ adherence to fiduciary responsibilities and consideration of shareholder benefits. Further evidence shows that the reduction in cost stickiness caused by religion increases firm value. Overall, our findings suggest that religion reduces the wedge between a firm’s actual and optimal resource commitments, which helps to improve firm value and resource allocation efficiency.

Using U.S. listed firms during the period from 1971 to 2010, this paper investigates the effect of religion on corporate cost behavior. We find that religion mitigates cost stickiness induced by agency or behavioral biases of managers. This result holds for several robustness tests that address endogeneity concerns. The mitigating effect of religion on cost stickiness is through the channel of reducing top managers’ overconfidence and optimistic bias regarding future demand change and promoting managers’ adherence to fiduciary responsibilities and consideration of shareholder benefits. Further evidence shows that the reduction in cost stickiness caused by religion increases firm value. Overall, our findings suggest that religion reduces the wedge between a firm’s actual and optimal resource commitments, which helps to improve firm value and resource allocation efficiency.

A nonlinear coupled wing model subject to unknown external disturbances is proposed in this paper. Since the model is modeled by partial differential equations, the traditional control design scheme based on the ordinary differential equation model is not applicable, and the control law design becomes very complex. In this paper, a new antidisturbance boundary control scheme based on a finite time convergent disturbance observer is proposed. The control laws are designed based on the new disturbance observers to make the external disturbance errors converge to zero in a finite time and ensure the uniformly bounded stability of the controlled system. Finally, the effectiveness of the controllers and the finite-time convergence of disturbance errors are verified by the simulation and comparison.

Lung nodules are an early symptom of lung cancer. The earlier they are found, the more beneficial it is for treatment. However, in practice, Chinese doctors are likely to cause misdiagnosis. Therefore, deep learning is introduced, an improved target detection network is used, and public datasets are used to diagnose and identify lung nodules. This paper selects the Mask-RCNN network and uses the dense block structure of Densenet and the channel shuffle convolution method to improve the Mask-RCNN network. The experimental results prove that proposed algorithm is extremely effective.

Learner control is an important instructional design in video-based learning. This study assessed two conditions: a full learner control where learners direct their learning path, and a hybrid learner control where learners follow the instructor-set path but still enjoy certain aspects of control. Two groups of university students participated in this study by learning statistics through online video courses. The findings show that the full learner control condition attained higher learning performance than the hybrid learner control condition, but spent more time than the latter. The full learner control condition scored higher than the hybrid condition in the difficult sections of video-based learning; but no significant difference was found in the easy section. There was a significant difference between the two conditions in learning agency, but no differences in cognitive load and affective and cognitive engagement. Hierarchical regression analysis indicated differences between the full and the hybrid learner control conditions in the factors predicting overall scores. The findings carry important contributions and implications for the research and practice of instructional designs in online video-based learning such as MOOCs.

Transcranial direct current stimulation has been widely utilized in research settings and modulates brain activity. The application of anodal tDCS on the prefrontal cortex has indicated improvement in cognitive functioning. The cingulate cortex, situated in the medial aspect of the prefrontal cortex, has been identified as a core region performing cognitive functions. Most of the previous studies investigating the impact of stimulation on the prefrontal cortex stimulated the dorsolateral prefrontal cortex, however, the impact of stimulation on cingulate has not been explored. The current study investigates the effect of stimulation on the resting-state functional connectivity of the anterior cingulate cortex with other regions of the brain and changes in behavioral results in a color-word Stroop task, which has repeatedly elicited activation in different regions of the cingulate. Twenty subjects were randomly assigned to the experimental and sham group, and their medial prefrontal area was stimulated using MRI compatible tDCS. Resting-state functional magnetic resonance imaging and cognitive Stroop task were monitored before, during, and after the stimulation. Neuroimaging results indicated a significant decrease in resting-state functional connectivity in the experimental group during and after stimulation as compared to before stimulation in two clusters including right insular cortex, right central operculum cortex, right frontal operculum cortex and right planum polare with the left anterior cingulate cortex selected as the seed. The behavioral results indicated a significant decrease in reaction time following stimulation in the experimental group compared to the sham group. Moreover, the change in functional connectivity in subcortical regions with L-ACC as the seed and change in RT was positively correlated. The results demonstrated that ACC has a close functional relationship with the subcortical regions, and stimulation of ACC can modulate these connections, which subsequently improves behavioral performance, thus, providing another potential target of stimulation for cognitive enhancement.Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT04318522.

Background and PurposeTranscranial direct current stimulation is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS by analyzing the ctDCS-induced functional network alterations.MethodsEither the active 5-day, −1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging data. A support vector machine prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers.ResultsOur results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS. Our prediction model achieved the mean prediction accuracy of 68.3% after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance.ConclusionThe study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome.

COVID-19 has made it difficult to adopt traditional face-to-face psychological intervention under this situation because of the blocked down and social distancing, which brings big psychological crisis to the public among the global. To explore the emotional change of the public in China at the outburst of the pandemic at different phases, to establish an online working platform and create a new model of an online intervention to hold public emotions under pandemic, and test its effectiveness, so to give advisement for government emergency management system. We established an online organization to work for this program ad innovated a model of online group counseling with online emotional support accompany group right after the outburst of a pandemic. We analyzed 53 OESAGs from February 10 to April 9, including 555 application forms, 253 feedback from members, and 139 feedback from group leaders by using NVivo and SPSS to explore the evolution and characteristics of public emotion during COVID-19 and the effectiveness of OESAG. Our results showed that the emotional changes of members ranged from shock to depression to positive. The public's emotions swiftly changed from stress, anxiety, and isolation, to the hope of returning to work or finding a job during the pandemic with the help of OESAG. OESAG has effectively regulated the negative emotions of members by conducting psychological crisis intervention to provide members a space to communicate with each other, especially the female and frontline staff. Policy makers can set up an online systematic psychological crisis intervention system as soon as possible to make up for the lack of psychological assistance in the emergency management system.

“Welcome Back Tourism” is an important marketing strategy to help overseas Tourism destinations quickly recover from the crisis and enhance their core competitiveness. How to translate the memorability of tourists to revisit intention is the core key to open “Welcome Back Tourism.” This study takes local residents in Guangzhou, Shenzhen and Foshan as the research objects, and tries to explore the influence relationship between memorability of a previous travel experience, nostalgia, destination attachment and revisit intention. The results of 291 valid data showed that memorability of a previous travel experience had positive influence on revisit intention; Nostalgia has a positive effect on destination attachment. Nostalgia and destination attachment play a mediating role in the influence of memorability of a previous travel experience on revisit intention. The contributions and management Recommendations of these findings are discussed.